Embodiments of the present specification relate generally to image registration, and more particularly to systems and methods for synchronization of time varying longitudinal data sets corresponding to multiple parameters from a plurality of image modalities at a plurality of time instants.
Images of an organ of interest acquired over time by an imaging device are typically similar to one another. However, to establish feature correspondence, it is required to align these images with respect to one another. Synchronization of images may be required when a plurality of imaging modalities acquire images from an organ of interest. In general, image registration techniques are employed in various areas such as remote sensing, recognition, tracking, and diagnostics. In medical field, image registration enables the work of clinicians, radiologists and surgeons in planning and performing surgeries, for example. In applications such as tracking and remote sensing, image registration techniques are used to automatically recognize and tack moving objects.
Image registration techniques typically include identifying a geometric transformation that aligns images into a same coordinate space. One or more geometric transformations used in image registration may be obtained by optimization techniques. In particular, optimization techniques employ a registration model characterized by a plurality of parameters, known as registration parameters. The model determines the type of registration, such as a rigid registration, affine registration, or deformable registration. The registration parameters include, but not limited to, a scaling factor, a linear shift, an angular shift and a transformation function.
In conventional registration techniques, a reference image is used to align a plurality of images. For example, in some of the existing techniques, a pair-wise alignment is achieved and one of the images of the pair is used with a new image for registration. In other techniques, a template based registration is employed. However, these techniques are influenced by selection of the reference image or an average image model.
Group wise registration methods attempt to mitigate uncertainties associated with any one image by simultaneously registering all images in a population. Group-wise registration techniques incorporate information from all images in registration process and eliminate bias towards a chosen reference frame. Synchronization of longitudinal data requires group-wise registration of images in batches. Group-wise registration helps a clinician to analyze a region of interest (ROI) for temporal variations. When a new batch of images is included in the longitudinal data, conventional group-wise registration provides temporal trends inconsistent with the previous results obtained from a subset of longitudinal data. Newer techniques are needed to avoid inconsistencies in interpretation while synchronizing the longitudinal image data.